1. Technical Field
The present invention involves a new system and process for automatically assigning scores to predictor values for measuring the influence of each predictor/variable value pair on a prediction of likely user choices.
2. Related Art
By way of background, collaborative filtering or recommender systems typically use various probabilistic methods in combination with one or more databases comprised of user preferences to predict additional topics, items, or products a new user might like. Generally, the goal of such systems is to predict the utility of items to a particular user based on a database of user preferences or votes from a sample or population of other users. Either of two general classes of collaborative filtering algorithms, e.g., memory-based algorithms or model-based collaborative filtering, is often used for making such predictions. One limitation of such systems is that while they are useful for predicting user preferences, they are not useful for determining the contribution or influence of particular user preferences or votes from the population of users on particular user preference predictions.
For example, a probabilistic model can be used for making predictions in a movie-recommendation application that suggests movies to users based on the other movies that they have seen. One method of implementing such a system is to build a probabilistic model over the set of variables M={M1, . . . , Mn}, where Mi is a binary variable denoting whether or not a user has seen the ith movie. The model is constructed such that, for each movie i, the conditional probability, p(Mi=watched |M {Mi}) is extracted for each movie. In general, such a system is used to recommend movies as follows: for each movie Mi that a user has not watched, the probability that the user would have watched the movie is calculated based on the assumption that it is unknown whether the user has actually watched movie Mi. Such a system then recommends those movies that have the highest posterior probability. However, while this system recommends movies based on the probability that a user will want to watch the movies, it fails to answer the question as to why the user might want to watch the movies. In other words, such a system is unable to determine which other movies from the set M={M1, . . . , Mn} were most influential in making the particular probabilistic recommendations to the user.
Consequently, what is needed is a system and process for determining which elements of a probabilistic model have the greatest influence on particular probabilistic predictions or recommendations computed from the probabilistic model. Determining which elements have the greatest influence on the predictions or recommendations allows the question to be answered as to why such predictions or recommendations were made.
In general, the present invention solves the aforementioned problems, as well as other problems that will become apparent from an understanding of the following description by automatically assigning xe2x80x9cscoresxe2x80x9d to the predictor/variable value pairs of a conventional probabilistic model to measure the relative impact or influence of particular elements of a set of topics, items, products, etc. on particular predictions. In particular, these scores measure the relative impact, either positive or negative, that the value of each individual predictor variable has on the posterior distribution of the target topic, item, product, etc., for which a probability is being determined. These scores are useful for understanding why each prediction is made, and how much impact each predictor has on the prediction. Consequently, such scores are useful for explaining why a particular recommendation was made.
For example, where a probabilistic model recommends particular movies to a user based upon other movies that the user has seen, an application embodying the present invention provides information as to what other movies were most influential in making the particular recommendations. Thus, for example, where the predictor/variable value pairs associated with watching movie j, and with not watching movie k, have the highest scores, these predictor/variable value pairs are provided as the most influential factors in determining whether the user should be provided with a recommendation to watch movie i. In one embodiment, such information is provided as a human-readable or xe2x80x9cnatural languagexe2x80x9d explanation such as, for example, xe2x80x9cMovie i was recommended to the user because the user watched movie j, but did not watch movie k.xe2x80x9d Further, any number of the top most influential topics, items, products, etc., may be provided for the purpose of explaining particular recommendations. In other words, the top n most influential predictor/variable value pairs can be provided for the purpose of explaining particular probabilistic recommendations. Clearly, the most positive influences as well as the most negative influences may be identified.
In accordance with preceding discussion, a system and method according to the present invention operates to automatically assign scores to members of a set of at least one predictor/variable value pair representing likely user choices for determining the effect or influence of those predictor/variable value pairs on predictions of one or more likely user choices. This is accomplished by first obtaining a set of user preferences for a particular user. These preferences are obtained either explicitly or implicitly using conventional techniques.
For example, one common method for explicitly obtaining user preferences involves asking a user to rate particular objects, such as topics, items, products, books, movies, food, drinks, etc., on some predefined schedule or list. One example of implicitly obtaining user preferences is to simply identify whether a user has used, purchased, viewed, etc., such objects. Further, another common method for implicitly obtaining user preferences involves observing user behavior in order to impute particular preferences to that user. For example, the idea here is that by watching a particular movie, the user is showing a preference for that movie over the movies that the user didn""t watch. Examples of such observation include observing a user""s Internet web browsing behavior, i.e., what items or objects does the user look at while browsing; observing a user""s purchasing history to see what the user buys, when the user buys, and where the user buys; and observing other available informational patterns. Clearly, any conventional technique for either implicitly or explicitly determining user preferences, or some combination of both implicit and explicit determination of user preferences, may be used to determine a set of user preferences.
Next, once the user preferences have been determined using conventional techniques, the probability of each variable/variable value pair is computed, again using conventional techniques using the set of user preferences in combination with a probabilistic model. Simple examples of variable/variable value pairs include xe2x80x9cMovie 1/watched,xe2x80x9d or xe2x80x9cBook 5/not read.xe2x80x9d The probabilistic model can be any conventional type, including, for example, dependency networks and Bayesian networks, so long as the probabilistic model can be used to compute the probability of variable/variable value pairs. Next, at least one prediction of likely user choices is automatically computed based upon the variable/variable value pairs using conventional techniques.
To differentiate the variable/variable value pairs from the predictions of likely user choices, the term xe2x80x9cpredictor/variable value pairxe2x80x9d is used in place of xe2x80x9cvariable/variable value pairxe2x80x9d to refer to a variable that is used in the probabilistic model to predict the specific target for which a prediction is being made. Further, it should be noted that the set of all predictor/variable value pairs that are evaluated is preferably restricted by those pairs that are observed or implied for a particular user. For example, if it is known that a user has watched Movie 5, no evaluation is made of the effect on a given prediction of the user not watching Movie 5.
The system and method of the present invention then calculates xe2x80x9cexplanation scoresxe2x80x9d for at least one of the predictor/variable value pairs for each prediction. These scores are then used for determining the influence of specific predictor/variable value pairs for each prediction based upon the scores calculated for specific predictor/variable value pairs. In a further embodiment, explanation scores are evaluated for either all predictor/variable value pairs, or most generally, for arbitrary sets of predictor/variable value pairs of size n.
This embodiment is useful for explaining when or why particular sets of predictor/variable value pairs had a greater influence than either individual predictor/variable value pairs, or than other sets of predictor/variable value pairs having lesser, equal or greater numbers of predictor/variable value pairs.
Specifically, as described above, the present invention provides a system and method for assigning an explanation score to an observed value of a predictor variable when predicting a state of a target variable. This explanation score is then used to choose the predictor/variable value pairs, or sets of predictor/variable value pairs, that individually had the greatest influence, either positive or negative, on the posterior probability of the given (i.e. recommended) state of the target variable. These predictor/variable value pairs are then presented, in one embodiment, as a simple list, or alternatively, as a natural language xe2x80x9cexplanationxe2x80x9d similar to the example provided above describing why Movie i was recommended to the user. Further, in a related embodiment, the least influential predictor/variable value pairs are determined by identifying those predictor/variable value pairs having the lowest explanation scores. This embodiment is useful for determining which predictor/variable value pairs, or sets of predictor/variable value pairs, individually had the least influence, either on the posterior probability of the given state of the target variable.
In addition to the just described benefits, other advantages of the present invention will become apparent from the detailed description which follows hereinafter when taken in conjunction with the accompanying drawing figures.